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2
Introduction
Fast growth of Internet usage Exponential increase of e-
commerce Lack of consensus definition of
online satisfaction Lack of standard, affordable and
accurate measure of online consumer satisfaction
3
Research Questions
1. Is the two-factor .com Satisfaction|Dissatisfaction approach significantly better than the traditional one-factor approach?
5
Research Questions
3. Do .com Satisfaction|Dissatisfaction facets provide more information than the summated .com Satisfaction and .com Dissatisfaction scales?
6
Research Questions
4. Is attitude toward the site a mediating variable between satisfaction and behavioral intentions?
7
Research Questions
5. What variables moderate the relationship between attitude toward the site and behavioral intentions?
8
Research Questions
6. Does the two-factor .com Satisfaction|Dissatisfaction approach perform significantly better than the traditional one-factor approach in the Expectancy-Disconfirmation with Performance model?
11
Two-factor .com Satisfaction|Dissatisfaction Concept .com
Satisfaction
.com Dissatisfaction
Lack of .com DissatisfactionLack of .com
Satisfaction
12
Data Collection Processes
Literature Review Identify initial item pool based on
earlier literature
13
Data Collection Processes
Depth Interviews (Web designers)
Supplement initial item pool; generate initial .com satisfaction|dissatisfaction model
14
Data Collection Processes
Pilot Survey Purify the .com sastisfaction|
dissatisfaction instrument Cross-checking the final .com
satisfaction|dissatisfaction instrument (questionnaire) with Depth Interviews (Web users) Informal Survey of Industry Literature
15
Data Collection Processes
Main Study Confirm the .com satisfaction|
dissatisfaction instrument; test competing models and test moderating effects of control variables
16
Data Collection Processes Main Study—Respondents
Three sources Students enrolled in SJMC and IDSc Adults referred by student participants Respondents recruited via Service Quality
Institute Listserv mailing list 697 responses (33 were dropped)
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Data Collection Processes Main Study—Web Sites
Half of the respondents were directed to name an e-commerce site they had positive experience with
Half of the respondents were directed to name an e-commerce site they has negative experience with
18
Findings (R1)1.Is the two-factor .com Satisfaction|
Dissatisfaction approach significantly better than the traditional one-factor approach? Tests of Semi-Independency Tests of Competing Models Relationships with Specific Behavioral
Intentions.
19
Findings (R1)Tests of Semi-Independency
.com Satisfaction and .com Dissatisfaction are semi-independent
.com S/D is the overlapping part of .com Satisfaction and Dissatisfaction
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Competing Model 1
Attitude Behavioral Intention
Traditional Satisfaction
.04
.21** .67**
Adjusted R2=.313 Adjusted R2=.118
22
Competing Model 2
.44**
.42**
-.41**
.65**
-.26**
Behavioral Intention
Attitude
.com Dissatisfaction
.com Satisfaction
Adjusted R2=.477 Adjusted R2=.421 Adjusted R2=.313 Adjusted R2=.118
23
Competing Model 3
.46**
.51**
-.36**
.50**
-.32**
Behavioral Intention
Attitude
.com Dissatisfaction
.com Satisfaction
.com Satisfaction
.19**
Adjusted R2=.479 Adjusted R2=.436
Adjusted R2=.313 Adjusted R2=.118 Adjusted R2=.477 Adjusted R2=.421
24
Findings (R1)Relationships with Specific
Behavioral Intentions .com Satisfaction correlates most
significantly with specific positive behavioral intentions
.com Dissatisfaction correlates most significantly with specific negative behavioral intentions
25
Therefore… The two-factor .com Satisfaction|
Dissatisfaction approach is significantly better than the traditional one-factor approach.
26
Findings (R2)2.What are the major facets of .com Satisfaction
and .com Dissatisfaction? .com Satisfaction .com Dissatisfaction
Bipolars Organization Service Quality Simplicity Accuracy
Positive Unipolars AttractiveForgiving Sense of Community Flexible Personalizable Responsive Bricks parallel clicks Considerate
Negative Unipolars Difficult to use Cheap looking Deceptive Complicated Violates privacy Inconvenient Violates design norms
27
Findings (R3)3.Do .com Satisfaction|Dissatisfaction facets
provide more information than the summated .com Satisfaction and .com Dissatisfaction scales? Regression analysis Bivariate correlation analysis
28
Findings (R3)
Behavioral Intention
AttitudeAll Facets
Adjusted R2=.521 Adjusted R2=.446
Adjusted R2=.479 Adjusted R2=.436
Adjusted R2=.313 Adjusted R2=.118 Adjusted R2=.477 Adjusted R2=.421
Regression analysis facets account for more variance than summated scales in explaining attitudes and behavioral intentions
29
Findings (R3)
Bivariate correlation analysis facets offer more informative and
meaningful associations with specific behavioral intentions
30
Findings (R3)Snapshot of some findingsI would like to visit this Web site again in the
future Top Significant Correlations
Service Quality Simplicity
Accuracy AttractiveOrganizationBricks parallel Clicks
31
Findings (R3)
Snapshot of some findingsI might send an email to express my
appreciation Top Significant Correlations
Sense of Community ResponsiveAttractiveService QualityPersonalizable
32
Findings (R3)Snapshot of some findingsI might convince my friends not to use this
Web site Top Significant Correlations
Deceptive Violates Design NormsViolates PrivacyCheap LookingComplicatedDifficult to Use
33
Therefore… .com Satisfaction|Dissatisfaction
facets do provide more information than the summated .com Satisfaction and .com Dissatisfaction scales.
34
Findings (R4)4.Is attitude toward the site a mediating variable
between satisfaction and behavioral intentions? 3-step Least-squares multiple
regression analysis com Satisfaction and .com Dissatisfaction (partial
mediation) are more important predictors of behavioral intentions than Traditional Satisfaction (full mediation).
35
Findings (R5)5.What variables moderate the relationship
between attitude toward the site and behavioral intentions? Moderated Multiple Regression Analyses
Brand Equity Monopoly Involvement Self-Efficacy
Internet Efficacy Online Shopping Efficacy
36
Moderating Variable Test
Attitude
HighLow
Beh
avio
ral
Inte
ntio
n3.0
2.8
2.6
2.4
2.2
2.0
1.8
Monopoly
High
Low
37
Moderating Variable Test
Attitude
HighLow
Beh
avio
ral
Inte
ntio
n 3.2
3.0
2.8
2.6
2.4
2.2
2.0
Involvement
Low
High
38
Findings (R6)6. Does the two-factor .com Satisfaction|
Dissatisfaction approach perform significantly better than the traditional one-factor approach in the Expectancy-Disconfirmation with Performance model? Path Analyses
39
.com Satisfaction
.com Dissatisfaction
Attitude Behavioral Intention
Behavior
.com S|DS Consequences of .com S|DS
Subjective Disconfirmation
Expectations
Antecedents of .com S|DS
Calculated Disconfirmation
Performance Outcomes
Findings (R6)
40
Findings (R6) Expectancy Disconfirmation with
Performance Model holds true in the e-commerce domain
Treating .com Satisfaction and .com Dissatisfaction as partially independent constructs increases model fit
The two-factor .com Satisfaction|Dissatisfaction approach yields more meaningful associations with antecedent variables
41
Theoretical Implications Produced an instrument that can be used in
future theoretically-oriented studies Proves that treating .com Satisfaction and .com
Dissatisfaction as partially independent concepts increases explanatory power
Shows that facet level analysis reveals important information
Indicates that Expectancy-Disconfirmation with Performance model works well in e-commerce domain
Enriches marketing theory by introducing insights from the MIS and job satisfaction arenas
42
Managerial Implications The instrument
Reliable, comprehensive, affordable and easy-to-apply
Uses Cost-Benefit Analysis Competitive Analysis Longitudinal Analysis